Dietary Protein Intake and Distribution Patterns of Well-Trained Dutch Athletes
Bibliographic record
Abstract
Dietary protein intake should be optimized in all athletes to ensure proper recovery and enhance the skeletal muscle adaptive response to exercise training. In addition to total protein intake, the use of specific proteincontaining food sources and the distribution of protein throughout the day are relevant for optimizing protein intake in athletes. In the present study, we examined the daily intake and distribution of various proteincontaining food sources in a large cohort of strength, endurance and team-sport athletes. Well-trained male (n=327) and female (n=226) athletes completed multiple web-based 24-hr dietary recalls over a 2-4 wk period. Total energy intake, the contribution of animal- and plant-based proteins to daily protein intake, and protein intake at six eating moments were determined. Daily protein intake averaged 108±33 and 90±24 g in men and women, respectively, which corresponded to relative intakes of 1.5±0.4 and 1.4±0.4 g/kg. Dietary protein intake was correlated with total energy intake in strength (r=0.71, p <.001), endurance (r=0.79, p <.001) and team-sport (r=0.77, p <.001) athletes. Animal and plant-based sources of protein intake was 57% and 43%, respectively. The distribution of protein intake was 19% (19±8 g) at breakfast, 24% (25±13 g) at lunch and 38% (38±15 g) at dinner. Protein intake was below the recommended 20 g for 58% of athletes at breakfast, 36% at lunch and 8% at dinner. In summary, this survey of athletes revealed they habitually consume > 1.2 g protein/kg/d, but the distribution throughout the day may be suboptimal to maximize the skeletal muscle adaptive response to training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".